What file formats are supported by the PyOpenMS skill?

PyOpenMS supports a vast array of formats including mzML, mzXML, TraML, mzTab, FASTA, pepXML, protXML, mzIdentML, featureXML, and consensusXML.

Can I use PyOpenMS for metabolomics research?

Yes, it provides specific workflows for untargeted metabolomics, including feature detection, retention time alignment, and annotation with compound databases.

Does PyOpenMS integrate with Pandas?

Yes, PyOpenMS objects like FeatureMaps can be easily converted to Pandas DataFrames using the get_df() method for further data analysis and visualization.

How does this skill handle peptide identification?

It integrates with popular search engines (Comet, Mascot, etc.) and provides tools for loading identification data and applying False Discovery Rate (FDR) filtering.

PyOpenMS Mass Spectrometry Analysis

Name: PyOpenMS Mass Spectrometry Analysis
Author: Activer007

byActiver007

Data Science & ML

Analyzes mass spectrometry data for proteomics and metabolomics using the PyOpenMS library.

About

PyOpenMS is a specialized Claude Code skill that provides a robust Python interface for computational mass spectrometry, enabling developers and researchers to automate complex workflows. It facilitates spectral signal processing, feature detection, and peptide/protein identification across standard formats like mzML, FASTA, and mzIdentML. By integrating seamlessly with the Python data science ecosystem—including Pandas and NumPy—this skill simplifies the transition from raw mass spectrometry data to actionable biological insights, making it indispensable for LC-MS/MS proteomics and metabolomics research.

Key Features

Advanced signal processing including Gaussian smoothing and centroiding
Integration with identification engines like Comet, Mascot, and MSGFPlus
Comprehensive file handling for mzML, mzXML, TraML, and FASTA formats
Automated feature detection and linking for quantitative analysis
0 GitHub stars
Seamless data export to Pandas DataFrames for advanced statistical analysis

Use Cases

Building automated LC-MS/MS proteomics data processing pipelines
Performing untargeted metabolomics preprocessing and compound annotation
Converting and filtering mass spectrometry data for machine learning models

About

Key Features

Advanced signal processing including Gaussian smoothing and centroiding
Integration with identification engines like Comet, Mascot, and MSGFPlus
Comprehensive file handling for mzML, mzXML, TraML, and FASTA formats
Automated feature detection and linking for quantitative analysis
0 GitHub stars
Seamless data export to Pandas DataFrames for advanced statistical analysis

Use Cases

Building automated LC-MS/MS proteomics data processing pipelines
Performing untargeted metabolomics preprocessing and compound annotation
Converting and filtering mass spectrometry data for machine learning models